title: A statistical model and simulator for cardiovascular pressure signals.
abstract: Physiological signal simulators are often used to conduct validation studies of commercially available devices such as oscillometric non-invasive blood pressure (NIBP) monitors. Numerous assessment studies have been conducted using simulators to validate commercial NIBP monitors. While there are several simulators commercially available to evaluate oscillometric NIBP devices, currently there are no simulators designed to validate invasive pressure signal devices. A statistical model and simulator for invasive cardiovascular pressure signals such as arterial blood pressure and intracranial pressure are described. The model incorporates the effects of respiration on pressure signals and can be used to generate synthetic signals with time and frequency domain characteristics matching any desired subject population. Additionally, the way that noise and artefacts typically present in real pressure signals should be modelled is described. The proposed statistical model is a useful tool for validation of algorithms designed to process or analyse biomedical pressure signals to estimate parameters of clinical interest such as the cardiac frequency, heart rate variability, respiratory frequency, and pulse pressure variation in the presence of noise. The model can be used to simulate signals in order to validate commercial devices that process and analyse invasive pressure signals.
authors.affiliation: Electrical Engineering Department, Oregon Institute of Technology, Beaverton, OR 97006, USA.
authors.forename: C
authors.initials: C
authors.lastname: Staats
authors.forename: D
authors.initials: D
authors.lastname: Austin
authors.forename: M
authors.initials: M
authors.lastname: Aboy
mesh_terms: D001794:Blood Pressure / Q000502:physiology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007427:Intracranial Pressure / Q000502:physiology*; D008365:Manometry / Q000379:methods*; D008954:Models, Biological